Mild lameness in horses is difficult to detect and monitor for changes using standard subjective evaluation methods such as visual observation of the horse in motion. The purpose of visual and riding-based lameness evaluations is to detect abnormal, asymmetric gait patterns. Biosensing technology could be used to both objectively measure asymmetric gait patterns and monitor a change in gait suggestive of lameness. These types of sensing instruments should be reliable (e.g., characterized by accuracy, repeatability, sensitivity, specificity, or the like) and easily applicable by both horse owners and veterinarians.
The main output from two commercially available lameness detection devices may be derived from two motion sensors such as discussed in U.S. Pat. No. 7,601,126 to Keegan et al and GaitCheck™ at www.gaitcheck.com, each hereby incorporated by reference herein. One sensor may be placed on top of either the head or withers and the second may be placed over the pelvis. The Keegan reference may measure vertical, 1-dimensional acceleration patterns of the head and pelvis. The motion data may be decomposed and simulated with a phase-shift equation (e.g., difference between first and second harmonics). Lameness location (e.g., limb source) and severity may be determined based on maximum and minimum positions of the head and pelvis as compared to reference (e.g. phase-shift) patterns collected from other horses. The measurement of head and torso motion may provide for an indirect assessment of normal gait and lameness limb motion patterns. Additionally, interference with the horse's natural head motion may occur when horse handlers pull on the lead rope during the gait evaluation session in order to slow an eager horse or to turn them on a circle during lunging.
Another type of biosensor system, as discussed in U.S. Pat. No. 7,673,587 to Davies, hereby incorporated by reference herein, may be designed to measure the motion of any body part of a human or animal. The system may have been designed for a wide variety of applications including avoiding injury and improvement in conditioning of equine athletes, in addition to monitoring the technique of horseback riders. The system may provide attachment of inertial sensors to various locations, such as limb segments or the torso of animals. The system may analyze all gaits (including walking, trotting, cantering, and/or galloping) under various trajectories experienced during riding (such as performing sharp turns and/or jumping over obstacles). However, the all-encompassing approach to the system may hamper sensitivity and repeatability in detecting and monitoring subtle lameness. As the devices were not designed to be mounted to specific locations, secure attachment and proper orientation may be problematic. A high volume of data collected can also lead to spurious correlations that may be an artifact of the data and neither biologically nor clinically insightful.
Non-uniformity in lower leg motion may provide sensitive lameness indicators, especially for subclinical lameness cases. Gait kinematics involves the study of inertial metrics that may characterize limb segment motion as the leg cycles through a stride. This limb motion is clinically relevant if used to detect abnormal gait patterns symptomatic of lameness as well as monitor the progress of mitigating treatments. Each stride is a full cycle of limb motion, perhaps including stance and swing phases. The stance phase (e.g. limb retraction backward) may start when the hoof impacts the ground and end upon completion of the break-over event. The break-over event may begin when the heel lifts off the ground, continue as the hoof rotates, and end as the toe lifts off the ground. The swing phase (e.g., limb protraction forward) may start with the toe lifting off the ground and end with the leg descending towards the ground just prior to impact.
Biosensors attached to horse's limbs should not only be able to sustain high acceleration and vibration, but the sensors should be tuned to measure both high and low signal ranges and frequencies adequately. This may be necessary for gaining a full understanding of equine limb inertia patterns, but may also present challenges with separating true signals from spurious signals (e.g. vibrations due to poor coupling and the like), along with interpreting signal data. A common approach to overcome these challenges may be to filter the signals. When filtering is applied, the signal amplitudes and phase are affected in such a way that analytic power may be diminished and the magnitudes may only be used for relative comparisons, rather than indicating exact absolute signal values, such as discussed in U.S. Pat. No. 8,715,208 to Hodgins, hereby incorporated by reference herein. Filtering may lead to abstracting the data away from the true signal and imposing bias in the amplitude and phase of the raw signal. The degree of filtering applied should take into consideration the objective of the signal processing. The impact of losing data from filtering should be weighed against the ability to summarize signals in a manner that makes it easier to detect common patterns.
One type of inertial measurement device such as discussed in Olsen et. al., 2013 and Olsen et. al., 2012, each hereby incorporated by reference herein, may have been designed for application to the lower limb for the main purpose of evaluating neurologic disorders in horses at the walking gait. The walk instead of the trot may be considered, and the scope of the system does not include lameness evaluation. Data collected at 200 Hz with this system undergoes the application of both low pass Butterworth filtering and wavelet symlets decomposition filters, which may obfuscate subtle gait abnormalities as discussed previously. Additionally, the relatively low sampling rate (i.e. 200 Hz) may lead to missing rapidly changing signals with higher frequency content (e.g. during impact and breakover or the like).
Another type of device was designed for attachment of the sensors to each hoof as discussed in U.S. Pat. Pub. No. US2007/0000216 to Kater et al, hereby incorporated by reference herein. The type of sensors used on this device (e.g. piezoelectric accelerometer) have limited low frequency response, rendering them insensitive to accurate measurement of limb motions during swing and weight bearing phases which are predominated by slowly changing signals. Measurement of motion throughout the stride that is sensitive to high frequency as well as low frequency signals may be helpful for accurate extraction of stride angular and displacement metrics.
The “temporal limb phasing” output as mentioned in the Hodgins reference may focus on the relative limb timing events and motion waveforms (e.g. a high level abstraction) correlated across two limbs. Phasing differences across multiple strides are graphed as linear plots, without segmentation to separate strides or summarize motion findings from each leg. It may be useful to provide summary quantified metrics that are easier to conceptualize, as they may relate directly to segmented limb motion (e.g. stance and/or swing) as understood by veterinarians and horse owners.
In addition to providing meaningful insight into the presence and location (e.g. limb affected) of lameness, ease of use under field conditions may be important. Some systems require attachment of the devices to the horse or their tack (e.g., halter) with tape, perhaps in the Keegan and GaitCheck™ references. This can present a problem if the halter is loose or the coat isn't properly cleaned prior to attachment (perhaps resulting in the presence of dirt, loose hair, oil, or the like), as close coupling of the device to the horse may be significant to measurement accuracy and repeatability. Additionally, operation under varying temperature ranges is necessary for field use. It may be difficult to complete the calibration as needed to prevent synchronization drift for devices exposed to changing temperatures as perhaps in the Hodgins references.
Repeatability and accuracy are greatly improved if the devices are securely attached to the horse's legs. Attachment of devices to horse limbs presents unique challenges. The device should be as small and light as possible and designed to both conform to the limb and not interfere with normal locomotion. Device form factor and a secure method of attachment may better ensure device-to-horse coupling which may minimize spurious oscillatory motion resulting in improved signal integrity. The shape, size & weight (e.g., form factor) of the devices and limb attachment methods described in past systems do not appear to be well suited for sustaining relatively high equine limb acceleration, vibration, impact forces, or the like. The signals resulting from these spurious vibrations become convolved with the true signal and require filtering for their removal. A limitation of this filtering is that it may corrupt the original signal in both amplitude and phase to varying degrees throughout the stride dependent on frequency content. Maximizing device to horse coupling and thereby minimizing vibration induced artifacts may be the most effective method of mitigating this limitation of the filtering process.
Signal data processing may be required prior to data analysis and even delivery of informative gait uniformity metrics. The computer equipment often needed to perform inertial signal processing and analysis may present a challenge for convenient operation in the field, perhaps under time and mobility constraints. The horse handler should ideally be able to carry the equipment in their pocket or a portable holster, perhaps with minimal manual equipment operation requirements, even while either riding or running with horse in-hand. Additionally, decisions should be made quickly regarding selection of appropriate diagnostic procedures during a lameness exam. A significant degree of automation may be required to efficiently process multiple signal streams into a paired-limb set, divide each stride into phases, and derive meaningful limb motion metrics. Fully automated features that support the ability to recognize and exclude signal artifacts (e.g. tripping, forging, change in gait, or the like) are desirable for producing repeatable, reliable and accurate motion metrics near real-time.
The biosensor system of the Davies reference may monitor rider and horse motion and may include a data transfer (e.g., communication hub) attached to the horse. The hub can receive, store and even transmit the sensor data to a computing subsystem. This additional computing subsystem, necessary to analyze and report the data, could be difficult to carry and operate under field conditions and may add to overall system complexity. The limb motion biosensor systems described in the Hodgins and Olsen references may also require computer transport and operation in the field for data processing and analysis.
Therefore, it is desirable to provide an inertial biosensing instrument designed to alert horse owners of potential lameness and inform lameness diagnosis by equine veterinarians that can provide objective, quantified evidence of lameness location, severity and type. This device can serve as a complementary tool for the assessment of subtle gait alterations which may deliver accurate, meaningful results and may be convenient to operate (e.g., mobile and automated) under demanding field conditions.